workshop 9

*The author of this computation has been verified*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Fri, 04 Dec 2009 04:49:01 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa.htm/, Retrieved Fri, 04 Dec 2009 12:50:36 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

workshop 9

Dataseries X:

» Textbox « » Textfile « » CSV «

0.6348 0.634 0.62915 0.62168 0.61328 0.6089 0.60857 0.62672 0.62291 0.62393 0.61838 0.62012 0.61659 0.6116 0.61573 0.61407 0.62823 0.64405 0.6387 0.63633 0.63059 0.62994 0.63709 0.64217 0.65711 0.66977 0.68255 0.68902 0.71322 0.70224 0.70045 0.69919 0.69693 0.69763 0.69278 0.70196 0.69215 0.6769 0.67124 0.66532 0.67157 0.66428 0.66576 0.66942 0.6813 0.69144 0.69862 0.695 0.69867 0.68968 0.69233 0.68293 0.68399 0.66895 0.68756 0.68527 0.6776 0.68137 0.67933 0.67922

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.6348 NA NA 1.00313027278422 NA 2 0.634 NA NA 0.994834781274025 NA 3 0.62915 NA NA 0.99833399575141 NA 4 0.62168 NA NA 0.992684890341518 NA 5 0.61328 NA NA 1.00800671165671 NA 6 0.6089 NA NA 1.00019875869377 NA 7 0.60857 0.618705075384565 0.62111125 0.996126016691156 0.983618890828938 8 0.62672 0.620355177460896 0.619419166666667 1.00151111047995 1.01025996521083 9 0.62291 0.617554088189666 0.617926666666667 0.999397050658114 1.00867278172514 10 0.62393 0.61801598442987 0.617050416666667 1.00156481178381 1.00956935697316 11 0.61838 0.617873430258851 0.61735625 1.00083773389975 1.00081986004955 12 0.62012 0.621533670198093 0.61944375 1.00337386598556 0.997725513088225 13 0.61659 0.624111292253955 0.62216375 1.00313027278422 0.987948796396886 14 0.6116 0.62059741873987 0.623819583333333 0.994834781274025 0.985502004249165 15 0.61573 0.623499513706585 0.62454 0.99833399575141 0.987538861641773 16 0.61407 0.62053766542009 0.625110416666667 0.992684890341518 0.989577320152336 17 0.62823 0.631153742439531 0.626140416666667 1.00800671165671 0.995367622430265 18 0.64405 0.627963538409851 0.62783875 1.00019875869377 1.02561687200961 19 0.6387 0.62800349669787 0.630445833333333 0.996126016691156 1.01703255373318 20 0.63633 0.635516803784674 0.634557916666667 1.00151111047995 1.00127958255468 21 0.63059 0.639380086945164 0.639765833333333 0.999397050658114 0.986252172808257 22 0.62994 0.646683273255152 0.645672916666667 1.00156481178381 0.974109005215377 23 0.63709 0.652883568222109 0.652337083333333 1.00083773389975 0.97580951797406 24 0.64217 0.660523942485404 0.658302916666667 1.00337386598556 0.972213054963092 25 0.65711 0.665376727908722 0.663300416666667 1.00313027278422 0.987575868583947 26 0.66977 0.665039590020826 0.6684925 0.994834781274025 1.00711297500202 27 0.68255 0.672753153331977 0.673875833333333 0.99833399575141 1.01456231995272 28 0.68902 0.674490089210152 0.679460416666667 0.992684890341518 1.02154206714418 29 0.71322 0.690082654808576 0.68460125 1.00800671165671 1.03352836798636 30 0.70224 0.689549943477455 0.689412916666667 1.00019875869377 1.01840338998296 31 0.70045 0.69067808545805 0.693364166666667 0.996126016691156 1.01414829100227 32 0.69919 0.696171655005708 0.69512125 1.00151111047995 1.00433563327750 33 0.69693 0.694528065446792 0.694947083333333 0.999397050658114 1.00345836931970 34 0.69763 0.694573512049266 0.693488333333333 1.00156481178381 1.00440052477918 35 0.69278 0.691344094272987 0.690765416666667 1.00083773389975 1.00207697691918 36 0.70196 0.689767691881997 0.687448333333333 1.00337386598556 1.01767596288069 37 0.69215 0.686563675211818 0.68442125 1.00313027278422 1.00813664484428 38 0.6769 0.678214104126339 0.681735416666667 0.994834781274025 0.998062405192779 39 0.67124 0.678711127424122 0.67984375 0.99833399575141 0.98899218368133 40 0.66532 0.673968102405314 0.678934583333333 0.992684890341518 0.987168380262434 41 0.67157 0.684355916677976 0.67892 1.00800671165671 0.981316861056683 42 0.66428 0.679008265310305 0.678873333333333 1.00019875869377 0.97830915165138 43 0.66576 0.676225127060875 0.678855 0.996126016691156 0.984524196688224 44 0.66942 0.680686206756208 0.679659166666667 1.00151111047995 0.983448751209612 45 0.6813 0.68065976570716 0.681070416666667 0.999397050658114 1.00094060839952 46 0.69144 0.683751186939271 0.682682916666667 1.00156481178381 1.01124504528489 47 0.69862 0.684507121503284 0.683934166666667 1.00083773389975 1.02061757730982 48 0.695 0.686956154695017 0.68464625 1.00337386598556 1.01170940132060 49 0.69867 0.687895748619886 0.685749166666667 1.00313027278422 1.01566262242750 50 0.68968 0.683767769292802 0.687317916666667 0.994834781274025 1.00864654780864 51 0.69233 0.686678248682717 0.687824166666667 0.99833399575141 1.00823056697678 52 0.68293 0.682223104505912 0.687250416666667 0.992684890341518 1.00103616469366 53 0.68399 0.691519904378279 0.686027083333333 1.00800671165671 0.989111080779303 54 0.66895 0.684701896744169 0.684565833333333 1.00019875869377 0.976994518608651 55 0.68756 NA NA 0.996126016691156 NA 56 0.68527 NA NA 1.00151111047995 NA 57 0.6776 NA NA 0.999397050658114 NA 58 0.68137 NA NA 1.00156481178381 NA 59 0.67933 NA NA 1.00083773389975 NA 60 0.67922 NA NA 1.00337386598556 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/1gc4v1259927339.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/1gc4v1259927339.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/2dz7u1259927339.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/2dz7u1259927339.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/3ge5m1259927339.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/3ge5m1259927339.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/4ml271259927339.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599274308biy29muglyolfa/4ml271259927339.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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